Data Wrangling and Visualization for Data Analyst Course Overview

Data Wrangling and Visualization for Data Analyst Course Overview

The "Data Wrangling and Visualization for Data Analyst" course is a comprehensive program designed to equip learners with the necessary skills to manipulate and present data effectively. Throughout the course, students will understand the data science workflow, grasp data wrangling and visualization concepts, and become proficient in using tools like R, Python, SQL, Power BI, Tableau, and Java. Learners will start by acquiring and extracting data from various sources, including databases and the web, and then move on to cleaning and preprocessing to ensure data quality. The course emphasizes the importance of exploratory data analysis (EDA) to uncover patterns and insights. Advanced modules will introduce interactive visualizations and specialized techniques like geospatial data visualization. Finally, the course culminates with lessons on how to effectively present insights through storytelling with data and create compelling data presentations and reports. This course will be valuable for those seeking to hone their data analysis skills and effectively communicate their findings.

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600

  • Live Online Training (Duration : 24 Hours)
  • Per Participant
  • Guaranteed-to-Run (GTR)
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♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

  • Live Online Training (Duration : 24 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

To ensure a successful learning experience in the Data Wrangling and Visualization for Data Analyst course, participants are expected to have the following minimum prerequisites:


  • Basic understanding of at least one programming language (preferably R, Python, or SQL).
  • Familiarity with fundamental concepts of data analysis and statistics.
  • Comfort working with spreadsheets and performing basic data manipulations in software such as Microsoft Excel.
  • A willingness to learn and adapt to new tools and technologies.
  • Basic computer literacy and the ability to navigate software interfaces.

Please note that while prior experience with specific data analysis tools (e.g., Power BI, Tableau) is helpful, it is not mandatory for enrollment in this course. The training is designed to introduce these tools and guide students through practical applications.


Target Audience for Data Wrangling and Visualization for Data Analyst

The course 'Data Wrangling and Visualization for Data Analysts' equips learners with key skills in data processing and graphical representation.


Target audience and job roles for the course include:


  • Aspiring Data Analysts
  • Junior Data Scientists
  • Business Analysts
  • BI Developers
  • Data Engineers
  • Data-driven Product Managers
  • Marketing Analysts
  • Research Analysts
  • Academic Researchers
  • Data Journalism Enthusiasts
  • IT Professionals looking to transition into data roles
  • Graduate students in computer science, engineering, or related fields
  • Professionals in finance, healthcare, and other sectors seeking data proficiency


Learning Objectives - What you will Learn in this Data Wrangling and Visualization for Data Analyst?

  1. Introduction to Learning Outcomes: Gain proficiency in data wrangling and visualization techniques, utilizing tools such as R, Python, SQL, Power BI, and Tableau to extract, clean, transform, and communicate data insights effectively.

  2. Learning Objectives and Outcomes:

  • Understand the complete data science workflow and the role of data wrangling and visualization within it.
  • Become familiar with key data wrangling concepts and visualization principles to prepare and present data effectively.
  • Acquire skills in using SQL for data extraction and performing complex queries on databases.
  • Learn to program in R and Python for data extraction, cleaning, and preprocessing tasks.
  • Master techniques for handling missing values, outliers, and inconsistent data to improve dataset quality.
  • Develop competence in feature selection, extraction, and the creation of derived variables to enhance data analysis.
  • Apply data transformation and normalization techniques to prepare datasets for exploratory data analysis (EDA).
  • Create informative and engaging visualizations that reveal underlying patterns and relationships in the data.
  • Utilize advanced visualization tools to construct interactive dashboards, geospatial visualizations, and network graphs.
  • Deliver compelling presentations and reports that effectively communicate data-driven insights and recommendations to stakeholders.